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Tracking Error Learning Control for Precise Mobile Robot Path Tracking in Outdoor Environment

Author(s)
Kayacan, Erkan; Chowdhary, Girish
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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Abstract
Abstract This paper presents a Tracking-Error Learning Control (TELC) algorithm for precise mobile robot path tracking in off-road terrain. In traditional tracking error-based control approaches, feedback and feedforward controllers are designed based on the nominal model which cannot capture the uncertainties, disturbances and changing working conditions so that they cannot ensure precise path tracking performance in the outdoor environment. In TELC algorithm, the feedforward control actions are updated by using the tracking error dynamics and the plant-model mismatch problem is thus discarded. Therefore, the feedforward controller gradually eliminates the feedback controller from the control of the system once the mobile robot has been on-track. In addition to the proof of the stability, it is proven that the cost functions do not have local minima so that the coefficients in TELC algorithm guarantee that the global minimum is reached. The experimental results show that the TELC algorithm results in better path tracking performance than the traditional tracking error-based control method. The mobile robot controlled by TELC algorithm can track a target path precisely with less than 10 cm error in off-road terrain.
Date issued
2018-08-11
URI
https://hdl.handle.net/1721.1/131766
Department
Senseable City Laboratory; Massachusetts Institute of Technology. Department of Urban Studies and Planning; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Publisher
Springer Netherlands

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